CoOL_6_calibration_plot: Calibration curve

In CoOL: Causes of Outcome Learning

Calibration curve

Description

Shows the calibration curve e.i. the predicted risk vs the actual risk by subgroups.

Usage

CoOL_6_calibration_plot(
  exposure_data,
  outcome_data,
  model,
  sub_groups,
  ipw = 1,
  restore_par_options = TRUE
)

Arguments

exposure_data	The exposure dataset.
outcome_data	The outcome vector.
model	The fitted non-negative neural network.
sub_groups	The vector with the assigned sub_group numbers.
ipw	a vector of weights per observation to allow for inverse probability of censoring weighting to correct for selection bias
restore_par_options	Restore par options.

Value

A calibration curve.

References

Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. 2022. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology <https://doi.org/10.1093/ije/dyac078>

Examples

#See the example under CoOL_0_working_example

CoOL documentation built on May 24, 2022, 5:04 p.m.